# Multi-view Remote Sensing Image Segmentation With SAM priors

arXiv: https://arxiv.org/abs/2405.14171 · HTML: https://mlsanity.com/paper/2405.14171/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 17 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [NeRF++: Analyzing and Improving Neural Radiance Fields](https://mlsanity.com/paper/2010.07492/) (2020)
- [In-Place Scene Labelling and Understanding with Implicit Scene Representation](https://mlsanity.com/paper/2103.15875/) (2021)
- [NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)](https://mlsanity.com/paper/2210.00379/) (2022)
- [Progressive Scale-aware Network for Remote sensing Image Change Captioning](https://mlsanity.com/paper/2303.00355/) (2023)
- [Implicit Ray-Transformers for Multi-view Remote Sensing Image Segmentation](https://mlsanity.com/paper/2303.08401/) (2023)

## Built on by (most cited first, 4 of 4)

- [A Unified Framework with Multimodal Fine-tuning for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2410.11160/) (2024)
- [FoBa: A Foreground-Background co-Guided Method and New Benchmark for Remote Sensing Semantic Change Detection](https://mlsanity.com/paper/2509.15788/) (2025)
- [Efficient Semantic Splatting for Remote Sensing Multi-view Segmentation](https://mlsanity.com/paper/2412.05969/) (2024)
- [ViRefSAM: Visual Reference-Guided Segment Anything Model for Remote Sensing Segmentation](https://mlsanity.com/paper/2507.02294/) (2025)

## Abstract (arXiv)

Multi-view segmentation in Remote Sensing (RS) seeks to segment images from diverse perspectives within a scene. Recent methods leverage 3D information extracted from an Implicit Neural Field (INF), bolstering result consistency across multiple views while using limited accounts of labels (even within 3-5 labels) to streamline labor. Nonetheless, achieving superior performance within the constraints of limited-view labels remains challenging due to inadequate scene-wide supervision and insufficient semantic features within the INF. To address these. we propose to inject the prior of the visual foundation model-Segment Anything(SAM), to the INF to obtain better results under the limited number of training data. Specifically, we contrast SAM features between testing and training views to derive pseudo labels for each testing view, augmenting scene-wide labeling information. Subsequently, we introduce SAM features via a transformer into the INF of the scene, supplementing the semantic information. The experimental results demonstrate that our method outperforms the mainstream method, confirming the efficacy of SAM as a supplement to the INF for this task.